Operational metric analysis techniques for computer systems with resource-consuming clients, such as virtual machines (VMs), are important to ensure that the clients are operating at desired or target levels. Virtual appliances or virtual applications (VAs), which are pre-packaged virtual machine images, can be run on various virtualization platforms and used for public, private and hybrid cloud environments. For example, virtual appliances include software components/stacks along with metadata about their anticipated aggregate resource requirements, e.g., amount of memory and/or number of processor frequency desired for the virtual appliances. Accurate estimates of resource requirements of virtual appliances can both influence resource settings, such as number of processors and amount of memory, of virtual appliances. Allocating insufficient resources to a virtual appliance can potentially impact the performance, reliability and stability of the virtual appliance, while allocating excessive resources to a virtual appliance is wasteful. In addition, accurate estimates of performance characteristics (e.g., latency and throughout) of virtual appliances can influence the deployment of virtual appliances.
Predicting or estimating resource usage and/or performance characteristics of a virtual appliance is a challenging task. Component interactions and application complexity can result in complex, non-linear relationships between virtual appliance performance/behavior and resource usage. In addition, the amount of data related to resource usage and/or performance characteristics of a virtual appliance can be enormous. Therefore, there is a need for an operational metric analysis of virtual appliances that can efficiently provide effective operational metric predictions for virtual appliances.